EP4107549B1 - Procédé de navigation d'un porteur à l'aide d'un filtre de kalman estimant un état de navigation du porteur - Google Patents

Procédé de navigation d'un porteur à l'aide d'un filtre de kalman estimant un état de navigation du porteur Download PDF

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Publication number
EP4107549B1
EP4107549B1 EP21710547.7A EP21710547A EP4107549B1 EP 4107549 B1 EP4107549 B1 EP 4107549B1 EP 21710547 A EP21710547 A EP 21710547A EP 4107549 B1 EP4107549 B1 EP 4107549B1
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European Patent Office
Prior art keywords
satellite
carrier
innovation
delta range
kalman filter
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EP21710547.7A
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German (de)
English (en)
French (fr)
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EP4107549A1 (fr
Inventor
Loïc Davain
Kévin HONORE
Clément MISANDEAU
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Safran Electronics and Defense SAS
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Safran Electronics and Defense SAS
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/22—Multipath-related issues
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering

Definitions

  • the present invention relates to the field of navigation methods using a Kalman filter.
  • a Kalman filter is a recursive estimator of data included in a state vector and evolving over time.
  • the state vector contains dynamic data of the carrier (for example: position, speed, acceleration, etc.). This method works in successive iterations, each iteration taking as input the state vector produced by the previous iteration.
  • Each iteration includes two fundamental steps: a prediction step (also called a propagation step), and an update step (also called a registration step in some applications).
  • a prediction step also called a propagation step
  • an update step also called a registration step in some applications.
  • the update step is based on observations provided by sensors.
  • the Kalman filter uses data from positioning signals transmitted by different satellites, then received by the GPS receiver, as observations.
  • a signal emitted by one of the satellites can reflect off an obstacle before reaching the carrier (as indicated by the dotted arrow shown in figure 1 ).
  • This obstacle can just as easily be the ground, known as “Ground Bounce”, a building, or even the fuselage of the plane itself. In this case we say that the signal has undergone a multi-path.
  • An aim of the invention is to estimate the navigation state of a carrier on the basis of data emanating from satellites, in a manner which is more robust to errors.
  • the method according to the first aspect may also include the following optional characteristics, taken alone or combined with each other when technically possible.
  • the kinematic data associated with the satellite is a pseudo distance between the carrier and the satellite.
  • the measured delta range is not used by the Kalman filter to update the navigation state of the wearer.
  • the test on the second innovation associated with the satellite is only implemented provided that the test result produced by the test on the delta range innovation indicates that the positioning signal did not follow a multi-path.
  • the Kalman filter implements tight inertial/satellite hybridization.
  • a computer program product comprising program code instructions for executing the steps of the method according to the first aspect, when this program is executed by a computer.
  • a navigation system 1 of a mobile carrier in space comprises a receiver 2 and a processing unit 4.
  • the carrier is any: it may be a land vehicle, a ship or an aircraft.
  • the receiver 2 is adapted to receive and process positioning signals previously transmitted by satellites 3 organized in constellation (GPS, GALILEO, etc.). Receiver 2 is known in itself.
  • the receiver 2 typically comprises at least one antenna for receiving the signals, and a processing chain for these signals operating in three phases: an acquisition phase, a code error tracking phase (DLL), and a tracking phase. in phase error (PLL).
  • This processing chain notably includes a loop filter and uses a replica signal which is correlated to the received signal.
  • the processing unit 4 is configured to implement a carrier navigation method using a Kalman filter.
  • the processing unit 4 comprises at least one processor configured to execute a program comprising code instructions for implementing the navigation method.
  • the navigation system also comprises an inertial measurement unit 6 configured to provide inertial data from the wearer to the processing unit 4.
  • the inertial measurement unit 6 is conventional: it comprises gyrometers and accelerometers (typically three of each type).
  • Each signal is encoded by a PRN code, and includes a sample of information. More particularly, these different components of the signal are characterized as follows.
  • the PRN code is defined by a code length of 1023 bits (chips), at a frequency of 1.023 Mcps, and each bit modulated by a rectangular waveform.
  • the duration of a PRN code is therefore 1ms. This period is denoted T R.
  • the information bits are periodic and modulated by a rectangular waveform.
  • s R t HAS ⁇ d t ⁇ ⁇ ⁇ vs t ⁇ ⁇ ⁇ cos 2 ⁇ f L 1 t + ⁇
  • the receiver 2 is thus configured to produce the pseudodistance between the carrier and the satellite 3 of one of the two types mentioned above, on the basis of the signal that it receives from this satellite 3, typically the pseudo-code distance.
  • a “delta range” between the carrier and a satellite is a piece of data closely linked to the Doppler frequency of a positioning signal received by the receiver 2 and emanating from the satellite 3 considered.
  • a delta range can be compared to a pseudo-phase distance.
  • f d is the Doppler frequency of the signal received by the receiver 2
  • ID designates the known notion of “integrated Doppler”
  • ⁇ T is also a period of observation of the delta ranges.
  • t0 is the instant of the start of phase tracking.
  • ⁇ ( t )
  • is the distance between satellite 3 and the antenna at time t and where ⁇ is the wavelength of the signal received by receiver 2.
  • Kalman filtering is a method for recursive estimation of data included in a state vector and evolving over time (this state vector is also more simply called “state”).
  • This data is dynamic data (position, speed, acceleration, etc.). This method works in successive iterations, each iteration taking as input the state vector produced by the previous iteration.
  • Each iteration includes two fundamental steps: a prediction step (also called a propagation step), and an update step (also called a registration step in some applications).
  • a prediction step also called a propagation step
  • an update step also called a registration step in some applications.
  • the prediction step transforms a state vector X(k/k) into an a priori state vector X(k+1/k) using a transition model that models the movement of the carrier between time k and time k+1.
  • a covariance matrix P(k/k) is also associated with the state vector X(k/k) .
  • This covariance matrix can be seen as representative of an uncertainty on the estimate constituted by the state vector X(k/k).
  • the prediction step also transforms the covariance matrix P(k/k) into a covariance matrix P(k+1/k), based on the transition model.
  • the update step transforms the a priori state vector X(k+1/k) into an a posteriori state vector X(k+1/k+1) on the basis of external measurements which are called “observations”, and combining these observations with an observation model which models the noise to which these observations are subject.
  • the covariance matrix P(k+1/k) is transformed into a covariance matrix P(k+1/k).
  • the observations provide additional information, therefore improving the estimate made up of the a priori data X(k+1/k) and P(k+1/k). This is how greater confidence can also be placed in the state X(k+1/k+1) a posteriori. This can be seen graphically by a “decrease in P” between the a priori state and the a posteriori state.
  • the extended Kalman filter method proposes to bring the system back to the case of the classical Kalman filter by linearizing and discretizing the equations in the vicinity of the estimated solution.
  • the Kalman filter of system 1 uses as observations at a time t K at least two different data for each satellite in the constellation: a “delta range” between the carrier and satellite 3, and another kinematic data associated with the satellite 3. These observation data are often dated to a time other than t K .
  • the time of dating of the observations is noted t r . This is the moment of reception of radio navigation data.
  • this other kinematic data is a pseudodistance between the carrier and the satellite 3 considered.
  • the data processed as observations by a conventional Kalman filter are used to update the a priori state vector, i.e. to produce an a posteriori state vector taking into account such observations.
  • Kalman filter used by system 1 Another (optional) specificity of the Kalman filter used by system 1 is that the delta range measured by receiver 2 between the carrier and a given satellite is not used to implement such an update. The role of the delta range is then limited to conditioning the use of the pseudodistance in the update.
  • the Kalman filter is also configured to implement tight satellite/inertial hybridization.
  • the Kalman filter uses not only data emanating from the receiver 2, and in particular pseudodistances, but also inertial data provided by the inertial measurement unit 6. These inertial data mainly make it possible to maintain the navigation which will be recalibrated by observations.
  • a method implemented by the system comprises the following steps.
  • the processing unit 4 generates an estimated delta range between the carrier and the satellite 3 at time t K , from the content of the a priori state vector associated with time t K (step 102). Without loss of generality, it is assumed below that the navigation filter is synchronous with the radio navigation data. The times of dating of navigation t K are therefore considered equal to the times of reception of observation data t r .
  • ⁇ t is the duration between two Kalman cycles (two resets).
  • the state vector associated with time t r is subject to an inverse prediction by the Kalman filter, so as to obtain a state vector in the past at time t r - ⁇ T.
  • the matrix ⁇ k is denoted as the transition matrix.
  • the inverse prediction is carried out by multiplication by the inverse transition matrix ⁇ k ⁇ 1 : X ⁇ k
  • k ⁇ k ⁇ 1 . X ⁇ k + 1
  • the processing unit 4 also obtains a delta range measured between the carrier and the satellite 3 at time t (step 104).
  • the measured delta range is provided by the receiver 2, which determines this measurement, according to a known method, on the basis of the positioning signal received by the receiver 2 and emanating from the satellite considered.
  • the receiver 2 relies in particular on the phase of the received signal to determine this measured delta range.
  • the processing unit 4 also obtains during step 104 a pseudodistance measured between the carrier and the satellite 3 at time t r .
  • the measured pseudodistance is provided by the receiver 2, which determines this measurement, according to a known method, on the basis of the positioning signal received by the receiver 2 and emanating from the satellite considered.
  • the a priori state vector has not yet been updated by the Kalman filter, and two observations have been obtained for a given satellite and for a time t r : a pseudo range between the carrier and satellite 3, and a pseudodistance between the carrier and satellite 3.
  • a delta range innovation associated with satellite 3 is then calculated by processing unit 4 (step 106).
  • a covariance of innovation in delta range associated with the same satellite as the “Inno” data is also calculated by processing unit 4.
  • the processing unit 4 implements a test on the “Inno” delta range innovation, in order to determine whether the signal received from the associated satellite has undergone a multi-path (step 108).
  • the test on innovation 108 includes the calculation by the processing unit 4 of a term being or dependent on a difference between the innovation in delta range “Inno”, and the covariance of the innovation in associated delta range Cov Inno .
  • the term obtained is compared to a predefined threshold.
  • the processing unit 4 generates during the test step 108 a test result indicating whether the received satellite signal from which the measured delta range comes has undergone a multi-path or not.
  • test result generated by the processing unit 4 is then positive, in the sense that this test result indicates that the received signal from which the measured delta range comes has undergone a multi-path.
  • the pseudodistance measured for the same satellite is not used as an observation by the Kalman filter to update the navigation state of the carrier, during the update step.
  • test result generated by the processing unit is then negative, in the sense that this test result indicates that the received signal from which the measured delta range comes has not undergone multi-path.
  • the processing unit calculates a second innovation associated with the satellite, relating to the measured pseudodistance (step 110).
  • the processing unit then implements a test on the second innovation (step 112), following the same logic as that described for the test on the delta range innovation (except that we are working here on pseudodistances).
  • This test on the second innovation produces a second test result indicating a level of likelihood of the measured pseudodistance.
  • This test is part of the known operation of Kalman filtering techniques. If the second result indicates that the pseudodistance is likely, then the measured pseudodistance is used as an observation by the Kalman filter to update the navigation state of the wearer, during the update step (step 114). . Otherwise, the pseudodistance is not used in this way (as in the case where the test result on delta range innovation indicates a multi-path).
  • the first condition results from test 108 on innovation in delta range carried out for satellite 3
  • the second condition results from test 112 on the innovation relating to the pseudodistance itself associated with the same satellite 3.
  • test 112 it is also advantageous to only implement test 112 on the innovation relating to the pseudodistance if the test result on the innovation in delta range turns out to be negative, for purposes of saving calculation.
  • steps 100 to 114 are repeated for signals emanating from different satellites. For example, consider the situation in which N satellites are tracked. N pseudodistances and N delta ranges are measured, N innovations in delta ranges are calculated, each being tested.
  • the process described above may be subject to other variations.
  • the pseudodistance between the carrier and a satellite, the use of which is conditioned during the update step, can be replaced by other kinematic data associated with the satellite.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Navigation (AREA)
EP21710547.7A 2020-02-20 2021-02-19 Procédé de navigation d'un porteur à l'aide d'un filtre de kalman estimant un état de navigation du porteur Active EP4107549B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR2001716A FR3107588B1 (fr) 2020-02-20 2020-02-20 Procédé de navigation d’un porteur à l’aide d’un filtre de Kalman estimant un état de navigation du porteur
PCT/FR2021/050301 WO2021165626A1 (fr) 2020-02-20 2021-02-19 Procédé de navigation d'un porteur à l'aide d'un filtre de kalman estimant un état de navigation du porteur

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EP4107549A1 EP4107549A1 (fr) 2022-12-28
EP4107549B1 true EP4107549B1 (fr) 2024-03-27

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EP21710547.7A Active EP4107549B1 (fr) 2020-02-20 2021-02-19 Procédé de navigation d'un porteur à l'aide d'un filtre de kalman estimant un état de navigation du porteur

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US (1) US20230094700A1 (zh)
EP (1) EP4107549B1 (zh)
CN (1) CN115104039A (zh)
FR (1) FR3107588B1 (zh)
WO (1) WO2021165626A1 (zh)

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Publication number Priority date Publication date Assignee Title
FR3147636A1 (fr) 2023-04-07 2024-10-11 Safran Electronics & Defense Procédé d’estimation d’un état d’un système dynamique à l’aide d’un estimateur fonctionnant par itérations successives

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US8600660B2 (en) * 2006-09-29 2013-12-03 Honeywell International Inc. Multipath modeling for deep integration
US8981992B2 (en) * 2009-10-12 2015-03-17 Qualcomm Incorporated Methods and apparatuses for selectively validating satellite positioning system measurement information
US8589072B2 (en) * 2011-04-13 2013-11-19 Honeywell International, Inc. Optimal combination of satellite navigation system data and inertial data
FR3034514B1 (fr) * 2015-04-01 2017-04-21 Sagem Defense Securite Procede de suivi de navigation d'un porteur moblile avec un filtre de kalman etendu
DE112017006954T5 (de) * 2017-01-30 2019-10-31 Mitsubishi Electric Corporation Positionsmessvorrichtung und Positionsmessverfahren

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WO2021165626A1 (fr) 2021-08-26
CN115104039A (zh) 2022-09-23
FR3107588A1 (fr) 2021-08-27
FR3107588B1 (fr) 2022-01-21
EP4107549A1 (fr) 2022-12-28
US20230094700A1 (en) 2023-03-30

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